"""simple docstring"""






import math
from dataclasses import dataclass
from typing import Optional, Tuple, Union

import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor
from .scheduling_utils import SchedulerMixin, SchedulerOutput

@dataclass
class lowerCamelCase		(__snake_case   ):






					'''simple docstring'''


					a 						=		42
					a 						=		42



class lowerCamelCase		(__snake_case     ,     __snake_case   ):






					'''simple docstring'''


					a 						=		1



					@register_to_config
					def __init__(    self      :					str ,			_snake_case      :					List[str] = 2000 ,			_snake_case      :					Union[str, Any] = 0.15 ,			_snake_case      :					Tuple = 0.01 ,			_snake_case      :					List[str] = 1348.0 ,			_snake_case      :					int = 1e-5 ,			_snake_case      :					List[str] = 1 ,			)				->							Dict:
									SCREAMING_SNAKE_CASE__			=					sigma_max

									# setable values
									SCREAMING_SNAKE_CASE__			=					None

									self.set_sigmas(_snake_case ,			_snake_case ,			_snake_case ,			_snake_case		)



					def       lowerCAmelCase_       (    self      :					Dict ,			_snake_case      :					int ,			_snake_case      :					Optional[int] = None		)				->							str:
									return sample



					def       lowerCAmelCase_       (    self      :					List[Any] ,			_snake_case      :					Tuple ,			_snake_case      :					List[str] = None ,			_snake_case      :					Union[str, Any] = None		)				->							Any:
									SCREAMING_SNAKE_CASE__			=					sampling_eps if sampling_eps is not None else self.config.sampling_eps

									SCREAMING_SNAKE_CASE__			=					torch.linspace(1 ,			_snake_case ,			_snake_case ,			device=_snake_case		)



					def       lowerCAmelCase_       (    self      :					List[Any] ,			_snake_case      :					Any ,			_snake_case      :					str = None ,			_snake_case      :					Optional[int] = None ,			_snake_case      :					str = None		)				->							List[Any]:
									SCREAMING_SNAKE_CASE__			=					sigma_min if sigma_min is not None else self.config.sigma_min
									SCREAMING_SNAKE_CASE__			=					sigma_max if sigma_max is not None else self.config.sigma_max
									SCREAMING_SNAKE_CASE__			=					sampling_eps if sampling_eps is not None else self.config.sampling_eps
									if self.timesteps is None:
													self.set_timesteps(_snake_case ,			_snake_case		)

									SCREAMING_SNAKE_CASE__			=					sigma_min * (sigma_max / sigma_min) ** (self.timesteps / sampling_eps)
									SCREAMING_SNAKE_CASE__			=					torch.exp(torch.linspace(math.log(_snake_case		) ,			math.log(_snake_case		) ,			_snake_case		)		)
									SCREAMING_SNAKE_CASE__			=					torch.tensor([sigma_min * (sigma_max / sigma_min) ** t for t in self.timesteps]		)



					def       lowerCAmelCase_       (    self      :					str ,			_snake_case      :					List[Any] ,			_snake_case      :					str		)				->							Optional[Any]:
									return torch.where(
									    timesteps == 0 ,			torch.zeros_like(t.to(timesteps.device		)		) ,			self.discrete_sigmas[timesteps - 1].to(timesteps.device		) ,			)



					def       lowerCAmelCase_       (    self      :					int ,			_snake_case      :					int ,			_snake_case      :					List[Any] ,			_snake_case      :					str ,			_snake_case      :					str = None ,			_snake_case      :					int = True ,			)				->							Tuple:






									if self.timesteps is None:
													raise ValueError(
													    "`self.timesteps` is not set, you need to run \'set_timesteps\' after creating the scheduler"		)

									SCREAMING_SNAKE_CASE__			=					timestep * torch.ones(
									    sample.shape[0] ,			device=sample.device		)  # torch.repeat_interleave(timestep, sample.shape[0])
									SCREAMING_SNAKE_CASE__			=					(timestep * (len(self.timesteps		) - 1)).long()

									# mps requires indices to be in the same device, so we use cpu as is the default with cuda
									SCREAMING_SNAKE_CASE__			=					timesteps.to(self.discrete_sigmas.device		)

									SCREAMING_SNAKE_CASE__			=					self.discrete_sigmas[timesteps].to(sample.device		)
									SCREAMING_SNAKE_CASE__			=					self.get_adjacent_sigma(_snake_case ,			_snake_case		).to(sample.device		)
									SCREAMING_SNAKE_CASE__			=					torch.zeros_like(_snake_case		)
									SCREAMING_SNAKE_CASE__			=					(sigma**2 - adjacent_sigma**2) ** 0.5

									# equation 6 in the paper: the model_output modeled by the network is grad_x log pt(x)
									# also equation 47 shows the analog from SDE models to ancestral sampling methods
									SCREAMING_SNAKE_CASE__			=					diffusion.flatten()
									while len(diffusion.shape		) < len(sample.shape		):
													SCREAMING_SNAKE_CASE__			=					diffusion.unsqueeze(-1		)
									SCREAMING_SNAKE_CASE__			=					drift - diffusion**2 * model_output

									#  equation 6: sample noise for the diffusion term of
									SCREAMING_SNAKE_CASE__			=					randn_tensor(
									    sample.shape ,			layout=sample.layout ,			generator=_snake_case ,			device=sample.device ,			dtype=sample.dtype		)
									SCREAMING_SNAKE_CASE__			=					sample - drift  # subtract because `dt` is a small negative timestep
									# TODO is the variable diffusion the correct scaling term for the noise?
									SCREAMING_SNAKE_CASE__			=					prev_sample_mean + diffusion * noise  # add impact of diffusion field g

									if not return_dict:
													return (prev_sample, prev_sample_mean)

									return SdeVeOutput(prev_sample=_snake_case ,			prev_sample_mean=_snake_case		)



					def       lowerCAmelCase_       (    self      :					Tuple ,			_snake_case      :					int ,			_snake_case      :					List[Any] ,			_snake_case      :					Dict = None ,			_snake_case      :					List[Any] = True ,			)				->							Dict:






									if self.timesteps is None:
													raise ValueError(
													    "`self.timesteps` is not set, you need to run \'set_timesteps\' after creating the scheduler"		)

									# For small batch sizes, the paper "suggest replacing norm(z) with sqrt(d), where d is the dim. of z"
									# sample noise for correction
									SCREAMING_SNAKE_CASE__			=					randn_tensor(sample.shape ,			layout=sample.layout ,			generator=_snake_case		).to(sample.device		)

									# compute step size from the model_output, the noise, and the snr
									SCREAMING_SNAKE_CASE__			=					torch.norm(model_output.reshape(model_output.shape[0] ,			-1		) ,			dim=-1		).mean()
									SCREAMING_SNAKE_CASE__			=					torch.norm(noise.reshape(noise.shape[0] ,			-1		) ,			dim=-1		).mean()
									SCREAMING_SNAKE_CASE__			=					(self.config.snr * noise_norm / grad_norm) ** 2 * 2
									SCREAMING_SNAKE_CASE__			=					step_size * torch.ones(sample.shape[0]		).to(sample.device		)
									# self.repeat_scalar(step_size, sample.shape[0])

									# compute corrected sample: model_output term and noise term
									SCREAMING_SNAKE_CASE__			=					step_size.flatten()
									while len(step_size.shape		) < len(sample.shape		):
													SCREAMING_SNAKE_CASE__			=					step_size.unsqueeze(-1		)
									SCREAMING_SNAKE_CASE__			=					sample + step_size * model_output
									SCREAMING_SNAKE_CASE__			=					prev_sample_mean + ((step_size * 2) ** 0.5) * noise

									if not return_dict:
													return (prev_sample,)

									return SchedulerOutput(prev_sample=_snake_case		)



					def       lowerCAmelCase_       (    self      :					int ,			_snake_case      :					int ,			_snake_case      :					int ,			_snake_case      :					Any ,			)				->							Optional[Any]:
									SCREAMING_SNAKE_CASE__			=					timesteps.to(original_samples.device		)
									SCREAMING_SNAKE_CASE__			=					self.discrete_sigmas.to(original_samples.device		)[timesteps]
									SCREAMING_SNAKE_CASE__			=					(
									    noise * sigmas[:, None, None, None]
									    if noise is not None
									    else torch.randn_like(_snake_case		) * sigmas[:, None, None, None]
									)
									SCREAMING_SNAKE_CASE__			=					noise + original_samples
									return noisy_samples



					def __len__(    self      :					int		)				->							List[Any]:
									return self.config.num_train_timesteps








"""simple docstring"""



def      UpperCAmelCase     (       _lowercase :   Optional[Any]	)						-> Tuple:


	"""simple docstring"""




	lowerCAmelCase_      							=			len(_lowercase	)
	lowerCAmelCase_      							=			sum(_lowercase	)

	lowerCAmelCase_      							=			[[False for x in range(s + 1	)] for y in range(n + 1	)]

	for i in range(1			,   n + 1	):
		lowerCAmelCase_      							=			True

	for i in range(1			,   s + 1	):
		lowerCAmelCase_      							=			False

	for i in range(1			,   n + 1	):
		for j in range(1			,   s + 1	):
			lowerCAmelCase_      							=			dp[i][j - 1]

			if arr[i - 1] <= j:
				lowerCAmelCase_      							=			dp[i][j] or dp[i - 1][j - arr[i - 1]]

	for j in range(int(s / 2	)			,   -1			,   -1	):
		if dp[n][j] is True:
			lowerCAmelCase_      							=			s - 2 * j
			break

	return diff